High-dimensional spatial profiling of the tumour microenvironment (TME) represents a critical frontier in understanding the biological determinants of therapy response and resistance across solid cancers. Current single-marker approaches, such as standard immunohistochemistry, are inadequate to capture the cellular complexity, spatial organisation, and functional heterogeneity that collectively govern tumour behaviour and immune evasion. There is therefore a pressing need to deploy comprehensive, discovery-based spatial proteomic platforms capable of simultaneously resolving multiple cellular and molecular programmes within intact tissue architecture.

In this study, we sought to address this gap by comprehensively characterising the TME in triple negative breast cancer (TNBC), one of the most aggressive and immunologically complex breast cancer subtypes. We applied a 68-plex spatial proteomic panel encompassing structural, tumour-immune, functional, and metabolic activity markers and the PhenoCycler-Fusion 2.0 workflow (Quanterix) to map the full biological landscape of the TNBC microenvironment at high resolution. Advanced image analytics (Visiopharm) were employed to precisely delineate tumour, stromal, and tumour-stromal interface regions, enabling compartment-specific analysis of cellular composition, spatial organisation, and protein expression. The study profiled a tissue microarray (TMA) comprising single-core biopsies from a clinically annotated cohort of TNBC patients, providing a spatially resolved, multi-dimensional portrait of the TME across this patient population.

Arutha Kulasinghe¹, Felicia Roland², Michelle Poulin², Ritu Mihani², Katherine Hales³, Daniel Winkowski, PhD³

1. Frazer Institute, University of Queensland
2. Akoya Biosciences, a Quanterix Company, Marlborough, MA
3. Visiopharm Corp., Broomfield, CO.

Phenoplex™ is Visiopharm’s end‑to‑end software solution for multiplex spatial biology analysis, enabling researchers to confidently visualize, segment, phenotype, and explore complex high‑plex tissue images within a single guided workflow. Built on AI‑powered cell detection and interactive verification steps, Phenoplex helps uncover meaningful biological insights, compare cohorts, and reproduce results across any plex level and imaging technology.

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The tumor microenvironment (TME) is a highly dynamic ecosystem that plays a central role in cancer progression, therapeutic resistance, and patient outcomes. However, its inherent complexity—driven by diverse immune, stromal, and malignant cell interactions—remains difficult to decode using conventional molecular profiling. Spatial proteomics technologies, such as Phenocycler Fusion/CODEX, now enable high-plex, protein-level mapping of cells within intact tissue architecture, providing unprecedented insights into both expression and localization patterns.

When coupled with artificial intelligence (AI), these approaches unlock a new dimension of discovery. AI methods, ranging from computer vision to graph-based modeling, can extract mechanistic insights, identify emergent spatial patterns, and uncover predictive signatures that are invisible to bulk or dissociated single-cell analyses. Our workflows integrate robust image preprocessing, segmentation, and spatial graph construction with interpretable AI platforms such as Visiopharm, enabling biologically meaningful target discovery.

Case studies illustrate how spatially resolved signatures can stratify patients for immunotherapy, reveal macrophage–tumor cell interactions and outperform conventional biomarkers in predicting clinical outcomes.

Despite significant challenges, such as batch effects, platform heterogeneity, and regulatory expectations for clinical deployment, a roadmap is emerging. Key elements include standardized panels and analysis pipelines, federated learning across multi-site cohorts, and integration of spatial biomarkers into companion diagnostics. By uniting computational, experimental, and clinical expertise, AI-driven spatial proteomics offers a direct path to more precise target selection, robust biomarker development, and improved patient stratification.

Ultimately, the convergence of spatial proteomics and AI has the potential to transform drug discovery and accelerate the development of next-generation cancer therapeutics that are both predictive and patient-centered.

Namrata Singh, PhD

Namrata Singh, PhD is a seasoned scientist and technical director at Dana-Farber Cancer Institute, with nearly three decades of experience spanning cancer research, spatial biology, and histological analysis. She also holds a concurrent research position at Harvard Medical School, where she contributes to cutting-edge immunohistochemistry and multiplex imaging studies.

Her expertise lies in high-dimensional tissue imaging and spatial analysis, leveraging advanced platforms such as Phenocycler Fusion, Vectra Polaris, Leica Bond RX, and analytical tools like Visiopharm, QuPath, and inForm. Namrata’s work bridges the gap between technology and translational research, enabling deeper insights into tissue architecture and disease pathology.

She earned her PhD and Master’s degrees from Delhi University, where she was a gold medalist.

Namrata is also an editor for research journals and holds numerous certifications in spatial biology technologies, including NanoString’s GeoMx DSP and nCounter platforms. Her contributions have been acknowledged internationally, including the second best paper presentation award from the IAEA.

The tumor microenvironment (TME) in non-small cell lung cancer (NSCLC) is shaped by both immune infiltration and metabolic reprogramming, which together modulate therapeutic response. We applied Akoya’s PhenoCode^TM Discovery IO60 Panel, combined with a spike-in PhenoCode Metabolic Module, to spatially characterize immunometabolic interactions in FFPE NSCLC samples, including both adenocarcinoma and squamous cell carcinoma (SqCLC) subtypes.

AI-powered image analysis and spatially resolved cellular phenotyping were performed using Visiopharm®, enabling high-dimensional, quantitative assessment of cell types and their spatial relationships within the immunometabolic contexture.

Alyssa Whitley¹⁺, Daniel Winkowski, PhD²⁺, Yue Hou¹, Matthew Moore¹, Ritu Mihani, PhD¹, Regan Baird, PhD² and Katherine J. Hales, PhD²

1. Akoya Biosciences, a Quanterix Company, Marlborough, MA
2. Visiopharm Corp., Broomfield, CO.

Imaging Mass Cytometry^TM (IMC^TM) technology enables high-dimensional, multiplexed imaging at subcellular resolution without autofluorescence or cyclic imaging. The Hyperion^TM XTi Imaging System supports three acquisition modes: Preview Mode (PM), Tissue Mode (TM), and Cell Mode (CM). PM provides rapid whole-slide scans for tissue overview within minutes, while TM captures entire tissue sections at 5-μm resolution, mapping over 40 biomarkers to reveal spatial heterogeneity. CM enables high-resolution, single-cell imaging of regions of interest (ROIs) identified in PM. Together with an automated slide loader, these modes support continuous, high-throughput tissue analysis.

Katherine Hales¹⁺, Smriti Kala², David Mason¹, James Mansfield², Regan Baird¹, Hiroyuki Suzuki³

1. Visiopharm A/S, Hørsholm, Denmark
2. Standard BioTools Canada Inc., Markham, ON, Canada
3. Fukushima Medical University, Fukushima, Japan

The ability to image tissue microenvironments (TME) at high-plex over an entire slide without requiring multiple staining steps allows for unprecedented insights into tissue architecture and the molecular mechanisms of immune and disease processes. Here we investigate a whole slide tissue section of high-grade colon adenocarcinoma, a hot tumor that contains prominent clusters of PD-L1 expression scattered throughout, using single-step high-plex staining and imaging at single-cell resolution followed by the analysis of single-cell phenotypes, tissue segmentation and spatial proximity and nearest neighbor analysis.

Fabian Schneider¹, James Mansfield¹, Edward Lo², Tad George², Joshua Nordberg²

1. Visiopharm A/S, Horsholm, Denmark
2. RareCyte, Seattle, WA